
Automated suggestion whether to use cross-validation or a specific validation set based on desired quality, training time and the amount of data available.

Automated suggestion of what percentage of the data should go into each of the sets.

Automated verification of best practices for a `good' split of the data. For instance, the distribution of labels and significant features in the three sets should be as similar as possible (stratified sampling [1]}).

Automatically hiding the test set away until needed, so it does not influence decisions in subsequent steps of the workflow like the choice of the model [2]}.

